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| Enrico Glaab; Anais Baudot; Natalio Krasnogor; Alfonso Valencia. |
| We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyze their enrichment in cancer mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes. |
| Tipo: Presentation |
Palavras-chave: Cancer; Genetics & Genomics; Bioinformatics. |
| Ano: 2011 |
URL: http://precedings.nature.com/documents/5639/version/1 |
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| Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor. |
| We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis. |
| Tipo: Presentation |
Palavras-chave: Cancer; Genetics & Genomics; Bioinformatics. |
| Ano: 2011 |
URL: http://precedings.nature.com/documents/5598/version/1 |
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| Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor. |
| DNA microarray experiments provide a means to understand cancer and genetic diseases on a molecular level, improve diagnosis and identify new drug targets. However, choosing appropriate data processing methods and parameters is a difficult and time-consuming task, particularly for researchers without prior experience in this field. 
We present *ArrayMining.net*, a free web-service for automatic microarray analysis to address these issues. ArrayMining.net covers several major areas in statistical microarray analysis - Feature Selection, Clustering, Prediction, Gene Set and Network Analysis - providing access to several algorithms for each of these tasks based on a single, easy-to-use interface. |
| Tipo: Poster |
Palavras-chave: Cancer; Genetics & Genomics; Bioinformatics. |
| Ano: 2011 |
URL: http://precedings.nature.com/documents/5552/version/1 |
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